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Success Story

Boosting Accuracy in Demand Forecasting for A Global Manufacturer

Impact at a Glance
  • 50% more accurate forecasts compared to the output of existing forecasting systems.
  • Optimized Inventory – SKU-level insights reduced excess stock, avoiding stockouts.
  • Lifecycle Forecasts – Dynamic adjustment to reflect SKU lifecycle changes
  • Improved Resource Planning – Precise demand prediction minimizes lead times.
About the client
A global leader in video surveillance systems offering world-class optical design, image processing, and cybersecurity technologies for 30+ years, the company manufactures a model of security cameras with video analytic technology. This technology can detect objects, such as people, vehicles, license plates, and faces, as well as classifying objects into categories such as age groups, gender, and color.
Business Challenge

Demand forecasting is a critical success factor in discrete manufacturing, particularly when lead times are long, and product lifecycles are short. The client, a discrete manufacturer of CCTV cameras, faced several challenges in managing demand forecasting due to data complexity.

a) Long Lead Times: Ordering raw material orders required a lead time of 3-4 months, adding pressure to ensure demand accuracy.

b) Problem of Data Availability: Sales, inventory, and distributed inventory data were not simultaneously available. Inventory data was updated only on the 5th of each month. Real-time data inferencing was not feasible due to infrastructure constraints.

c) Short Product Lifecycles: The client witnessed rapid changes in SKU demand. SKUs 6–12 months old experienced diminishing demand. Newer SKUs required accurate ramp-up forecasts to avoid lost sales opportunities.

d) Limitations in Internal Forecasting: Existing methods lacked the sophistication to adapt to evolving trends and patterns, with only moderate accuracy.

Solution: A Three-Tiered Forecasting Model Powered by Zunō.Predict

To address data unavailability challenges, we began by preprocessing the data, integrating historical information, and filling gaps using statistical methods. This ensured a reliable foundation for analysis despite incomplete data. Incremental updates were synchronized with inventory data, received on the 5th of each month, to refine forecasts dynamically.

Next, we built a robust three-layered prediction framework. Time-series modeling focused on localized SKU-level sales trends and historical data to generate precise forecasts. Hierarchical modeling combined inventory and sales pipeline data across distributed locations, ensuring regional accuracy.

Additionally, SKU-level adjustments accounted for product lifecycle dynamics by scaling down forecasts for aging SKUs while amplifying predictions for newer ones. Advanced forecasting techniques further enhanced the framework.

Time-series analysis uncovered trends, seasonality, and anomalies, while machine learning algorithms adapted to SKU lifecycle phases for greater accuracy.

Finally, scenario planning simulated demand under varying lead-time conditions, optimizing sourcing decisions to align inventory with business needs.

Zunō.Predict gives you precise insights to streamline your operations. It helps you reduce waste, optimize resources, and make sure every product in your portfolio pulls its weight. Want to know how it works and why it’s a game-changer? Let’s dive in and see how predictive analytics can give your business the edge it needs to succeed.

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